HerMES: SPIRE emission from radio-selected active galactic nuclei★
Bibliographic record
Abstract
We examine the rest-frame far-infrared emission from powerful radio sources with 1.4-GHz luminosity densities of 25 ≤ log(L1.4/W Hz−1) ≤ 26.5 in the extragalactic Spitzer First Look Survey field. We combine Herschel/SPIRE flux densities with Spitzer/Infrared Array Camera and Multiband Imaging Photometer for Spitzer infrared data to obtain total (m) infrared luminosities for these radio sources. We separate our sources into a moderate, 0.4 < z < 0.9, and a high, 1.2 < z < 3.0, redshift sub-sample and we use Spitzer observations of a z < 0.1 3CRR sample as a local comparison. By comparison to numbers from the Square Kilometre Array (SKA) Simulated Skies, we find that our moderate-redshift sample is complete and our high-redshift sample is 14 per cent complete. We constrain the ranges of mean star formation rates (SFRs) to be 3.4–4.2, 18–41 and 80–581 M⊙ yr−1 for the local, moderate- and high-redshift samples, respectively. Hence, we observe an increase in the mean SFR with increasing redshift which we can parametrize as ∼(1 + z)Q, where Q = 4.2 ± 0.8. However, we observe no trends of mean SFR with radio luminosity within the moderate- or high-redshift bins. We estimate that radio-loud active galactic nuclei (AGN) in the high-redshift sample contribute 0.1–0.5 per cent to the total SFR density at that epoch. Hence, if all luminous starbursts host radio-loud AGN we infer a radio-loud phase duty cycle of 0.001–0.005.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".